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Determining the stock status of snapper (Lutjanus sp.) using surplus production model: a case study in Banyuasin coastal waters, South Sumatra, Indonesia

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IOP Conference Series: Earth and Environmental Science 2020年第1期404卷

作者： Fauziyah A I S Purwiyanto F Agustriani W A E Putri Ermatita A Putra Marine Science Study Program Faculty of Mathematics and Natural Sciences Sriwijaya University Indonesia Information System Department Faculty of Computer Science Sriwijaya University Indonesia

Snapper (Lutjanus sp.) is an economically important fish for local fishermen in Banyuasin coastal water of South Sumatra. However, the current and historical stock of this species is still unknown. This study was aimed to estimate the stock status of Lutjanus sp. in the Banyuasin coastal waters. The annual catch and effort data were analyzed from 2008 to 2016. The different surplus production models were tested to obtain the best-fitted model based on the sign suitability test, model performance test, and multiple criteria analysis. The results indicated that the best-fitted model for Lutjanus sp. was the Fox model. The model had the best value for the determination coefficient (R2 = 97.2%), Nash-Sutcliffe Efficiency (-0.277), Mean Absolute Deviation (29.198), Mean Square Error (1,190.522), Root Mean Square Error (34.504), and RMSE-observations Standard Deviation Ratio (1.13), whereas the value of Mean Absolute Percentage Error (0.05) was the second-best value. The optimum effort (Eopt), maximum sustainable catch (CMSY), and total allowable catch were 22.236 trips/year, 623 ton and 498 ton/year, respectively. Based on plotting the effort and exploitation level (141%; 102%) in 2016, the stock status of Lutjanus sp. indicated depleting stock, the high fishing pressure and could encourage overfishing stock in the future.

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National Science Review 2025年第05期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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The development of bank applications for debtors' selection by using Naïve Bayes classifier technique

The development of bank applications for debtors' selection ...

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International Conference on Informatics, Engineering, Science and Technology, INCITEST 2018

作者： Ginting, S.L.B. Adler, J. Ginting, Y.R. Kurniadi, A.H. Study Program of Computer System Universitas Komputer Indonesia Jl. Dipati Ukur 112-116 Bandung40132 Indonesia Department of Mechanical Engineering Universitas Riau Kampus Bina Widya KM 12 Simpang Baru Pekanbaru28293 Indonesia

The purpose of this study is to create an application which functions automatically with high accuracy when analyzing bank customer data. This needed due to non-perforMing loans occurring frequently caused by the inaccuracy of credit analysts in the assessment of creditworthiness. This can be seen in the incident occurred in a public bank located in Bandung. This bank does not have the database that serves to accommodate data history and the method used in assessing creditworthiness is merely based on the simple statistical analysis. This leads to reduced accuracy and speed in the decision-making process. This research applies Naïve Bayes Classifier (NBC) method, a Data Mining technique. This helps credit analysts to select customers who are truly eligible to be given credit so that non-perforMing loan can be avoided. NBC calculates the probability of one class from each group of attributes and deterMines which class is most optimal. The accuracy of the NBC sampling test from 501 data is 94% compared to the decision made by a credit analyst. It can be concluded that this application is very helpful for credit analysts in recommending customers who are eligible for a loan to the bank's decision maker. © Published under licence by IOP Publishing Ltd.

关键词： Sales

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The Development of Bank Application for Debtors Selection by Using Naïve Bayes Classifier Technique

The Development of Bank Application for Debtors Selection by...

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International Conference on Informatics, Engineering, Science and Technology, INCITEST 2018

作者： Ginting, S.L.B. Adler, J. Ginting, Y.R. Kurniadi, A.H. Computer System Study Program Universitas Komputer Indonesia Jl. Dipati Ukur 112-116 Bandung40132 Indonesia Department of Mechanical Engineering Universitas Riau Kampus Bina Widya KM 12 Simpang Baru 5 Pekanbaru28293 Indonesia

The purpose of this study is to create an application that functions automatically with high accuracy when analyzing bank customer data. This needed due to non-perforMing loans occurring frequently caused by the inaccuracy of credit analysts in the assessment of creditworthiness. This can be seen in the incident occurred in a public bank located in Bandung. This bank does not have the database that serves to accommodate data history and the method used in assessing creditworthiness is merely based on the simple statistical analysis. This leads to reduced accuracy and speed in the decision-making process. This research applies Naïve Bayes Classifier (NBC) method, a Data Mining technique. This helps credit analysts to select customers who are truly eligible to be given credit so that non-perforMing loan can be avoided. NBC calculates the probability of one class from each group of attributes and deterMines which class is most optimal. The accuracy of the NBC sampling test from 500 data is 95% compared to the decision made by a credit analyst. It can be concluded that this application is very helpful for credit analysts in recommending customers who are eligible for a loan to the bank's decision maker. © Published under licence by IOP Publishing Ltd.

关键词： Sales

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The Application of Fuzzy Logic Method in the Debtors Eligibility Assessment system of Microfinance Institution

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IOP Conference Series: Materials Science and Engineering 2020年第1期879卷

作者： S L B Ginting M R M Rizky Y R Ginting Sutono Computer System Study Program Faculty of Engineering and Computer Science Universitas Komputer Indonesia Jl. Dipati Ukur 112-116 Bandung 40132 Indonesia. Department of Mechanical Engineering Universitas Riau Kampus Bina Widya KM 125 Simpang Baru Pekanbaru 28293 Indonesia.

Decision-making processes in determining loan eligibility are often subjective which leads to imprecise credit predictions. Due to inaccurate inquiry on prospective customers done by field survey officers of Bank Perkreditan Rakyat (BPR) Bandung City, it has experienced credit complications such as bad credits. Therefore, this study aims to help decision-makers in determining creditworthiness and preventing bad credits from occurring. To realize this solution, the study uses the Fuzzy Logic method to calculate the creditworthiness of each prospective loaner based on the inquiries done in the field survey. Fuzzy Logic is known to be a 'counting' methodology with varying words. In addition, it can implement human expertise into machine language with ease and adequately. Based on numerous testing performed, the results demonstrate a level of 90% in accuracy when inputting within the valid ranges of each fuzzy set and membership function. However, the level of accuracy is only based on the clarification result which is determined by a researcher and BPR director, not a general level of accuracy for other microfinance institutions. Nevertheless, the findings of this study prove the method has a high enough accuracy to support decision-makers in determining the loan eligibility of prospective loaners and through this application in the surveying process, survey workers can work more efficiently. Hence, in future has a higher chance of predicting bad credits from potential loaners.

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Smart monitoring apps for salvaging neolissochillus thienemanni sumateranus (batak heritage) from extinction

Smart monitoring apps for salvaging neolissochillus thienema...

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2017 International Conference on Electrical Engineering and computer Science, ICECOS 2017

作者： Harianja, Richad Pangaribuan, Nickholas Sinaga, Lambok Kisno, K. Siagian, Pandapotan Sembiring, Evita Rindani Institut Teknologi Del Computer Network Management Study Program Sitoluama22381 Indonesia Institut Teknologi Del Information System Study Program Del English Club-Writing Sitoluama22381 Indonesia

ISBN: (纸本)9781479976751

Ihan or Ikan Batak (Neolissochillus thienemanni sumateranus), the heritage of Batak tribe, is considered as vulnerable species of freshwater fish in Indonesia, especially at Lake Toba. This study aims to introduce SMAFIB application that is environment-friendly and helps the salvage of the fish from extinction. An application is built based on Django framework using Phyton programming language, which interacts with Raspberry pi through the internet to monitor the fish breeding and carries out all operations needed for fish conservation. A 14-day-experiment was carried out on 15 Ihan seed in a tailored aquarium equipped with the smart application. After two weeks, the result indicated that the success rate of breeding the endangered species using this system was 100% where all the fish put were survived. The implementation of the embedded system which is realized through mobile application has been successful in breeding the endangered species by considering and monitoring the essential aspects such as pH, temperature, feeding, water condition, and health risks. Time efficiency and yields are getting increased which reflect the increase in the economy for the farmers. SMAFIB application increases the efficiency of feeding time, monitoring time, and helps the salvage of the species in order to restore the custom and tradition of the Batak community as well as helps the improvement of the farmers' income without endangering the environment. © 2017 IEEE.

关键词： Embedded systems

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Modeling on clinical group decision support system for screening and working diagnosis acute respiratory infections

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IOP Conference Series: Materials Science and Engineering 2019年第1期508卷

作者： Muhammad Syaukani Sri Hartati Hari Kusnanto Suryo Guritno Study Program of Information Technology STMIK Indonesia Banjarmasin Indonesia Dept. of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Study Program of Publich Health Interests Health Management Information System Faculty of Medicine Universitas Gadjah Mada Yogyakarta Indonesia

There are cases of patients with Acute Respiratory Infection (ARI) which are not handled quickly and appropriately at emergency department (ED), thus there should be a medium that can assist medical personnel in making decisions. This research proposes the development model of Clinical Group Decision Support systems (CGDSS) named KLISPA models. KLISPA model can accommodate a screening process to the working diagnosis process of ARI by integrating Case Based Reasoning (CBR) and Groups Decision Support system (GDSS). Screening process completed by CBR using Nearest Neighbor method. This process helps medical personnel such as nurses, co asst doctors, and general practitioners to do screening to patients for determining the early solution to the disease. Meanwhile, the working diagnosis solved by GDSS uses the methods of Eckenrode, Extended TOPSIS and BoostVote. This process accommodates the needs of joint decision-making which involves groups of Decision Maker (DM) consisting of pediatrician, child lung specialist doctor, and the radiologist doctor.

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The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

JACC: Advances

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JACC: Advances 2024年第1期3卷 100759-100759页

作者： Pearson, Thomas A. Vitalis, Debbie Pratt, Charlotte Campo, Rebecca Armoundas, Antonis A. Au, David Beech, Bettina Brazhnik, Olga Chute, Christopher G. Davidson, Karina W. Diez-Roux, Ana V. Fine, Lawrence J. Gabriel, Davera Groenveld, Peter Hall, Jaclyn Hamilton, Alison B. Hu, Hui Ji, Heng Kind, Amy Kraus, William E. Krumholz, Harlan Mensah, George A. Merchant, Raina M. Mozaffarian, Dariush Murray, David M. Neumark-Sztainer, Dianne Petersen, Maya Goff, David College of Medicine and College of Public Health and Health Professions University of Florida Health Science Center Gainesville FL United States Division of Cardiovascular Sciences National Heart Lung and Blood Institute National Institutes of Health Bethesda MD United States Cardiovascular Research Center Massachusetts General Hospital and Broad Institute Massachusetts Institute of Technology Cambridge MA United States Center of Innovation for Veteran-Centered and Value-Driven Care University of Washington Seattle WA United States UH Population Health University of Houston Houston TX United States Johns Hopkins Medicine Institute for Clinical and Translational Research Baltimore MD United States Institute of Health System Science Feinstein Institutes for Medical Research Northwell Health New Hyde Park NY United States Feinstein Institutes for Medical Research Northwell Health Manhasset NY United States Urban Health Collaborative Drexel Dornsife School of Public Health Philadelphia PA United States Biomedical Informatics and Data Science Section Johns Hopkins University School of Medicine Baltimore MD United States Center for Health Care Transformation and Innovation University of Pennsylvania Perelman School of Medicine Philadelphia PA United States Department of Health Outcomes and Biomedical Informatics Institute for Child Health Policy College of Medicine University of Florida Gainesville FL United States Center for the Study of Healthcare Innovation Implementation & Policy VA Greater Los Angeles Healthcare System Los Angeles CA United States Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA United States Department of Computer Science University of Illinois Urbana-Champaign Champaign IL United States Center for Health Disparities Research (CHDR) University of Wisconsin School of Medicine and Public Health Madison WI United States Duke Molecular Physiology Insti

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce. © 2023 The Authors

关键词： data science health equity health promotion implementation science personalized medicine precision analytics

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Biodiversity of fish resources in Sungsang Estuaries of South Sumatra

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IOP Conference Series: Earth and Environmental Science 2019年第1期278卷

作者： Fauziyah Nurhayati S M Bernas A Putera Y Suteja F Agustiani Marine Science Study Program Faculty of Mathematics and Natural Sciences Sriwijaya University Indonesia Environmental Management Study Program Faculty of Agriculture Sriwijaya University Indonesia Faculty of Agriculture Sriwijaya University Indonesia Information System Department Faculty of Computer Science Sriwijaya University Indonesia Marine Science Department Faculty of Marine Sciences and Fisheries Udayana University Indonesia

The purpose of this study was to assess the fish biodiversity of Sungsang estuaries in South Sumatra. The species diversity, evenness, dominance, degree of similarity, and composition of fish communities as well as some physicochemical conditions were analyzed in order to establish the baseline data inventory of Sungsang estuaries. The results show that all of the physico-chemical parameters were in good condition for fish sustainability. Forty-eight (48) species of fish belonging to 29 families of freshwater, brackish water and marine sources were encountered in the water bodies. Johnius belangerii, Johnius amblycephalus and Setipinna taty were species with a relatively high abundance, but the appearance frequency of these species was high, medium and low, respectively. The value of the Shannon's diversity index for fish resources was classified as moderate (H'=1.477-2.708). The index value of evenness was classified as high (J'= 0.616 – 0.876), while the index value of dominance was classified as low (D = 0.097 – 0.382). This result indicate that the species diversity was good enough, the species spread was evenly distributed, that there was a stable community structure and no domination.

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Data Mining: The Classification Method to Predict the Types of Motorcycle Spare Parts to be Restocked

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IOP Conference Series: Materials Science and Engineering 2019年第2期662卷

作者： S L B Ginting Y R Ginting Sutono A Rakhman Computer System Study Program Faculty of Engineering and Computer Science Universitas Komputer Indonesia Jl. Dipati Ukur 112-116 Bandung 40132 Indonesia Department of Mechanical Engineering Universitas Riau Kampus Bina Widya KM 12 5 Simpang Baru Pekanbaru 28293 Indonesia

The research intends to create an application which is able to analyse sales data in a motorcycle company to predict the types of spare parts which should be stocked. This prediction is crucial since problems are often encountered while restocking. For instance, when there have been some imprecisions occurring in deciding regarding the types of spare parts to restock, the spare parts accumulate. It can cause inefficiency in terms of storage, the products quality deteriorates due to having been stored for too long, and sometimes the best-selling products are not available in the warehouse. This application is developed with Naïve Bayes Classifier (NBC) method which has a high accuracy in predicting future occurrences. This method works by calculating the probability value in each attribute class and determining the optimal probability value. From the test results, 4500 training data with 200 sample test data has 90% similarity with the results of the restock decision without application. For 500 test data, the similarity was 96%. It is proven that this method has a high accuracy so that it can help the decision makers solved the company problem in predicting the types of motorcycle parts to be restocked.

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